Results 151 to 160 of about 437,708 (290)

Association Between Liver Function Grade and Post‐Hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma: A Latent Class Analysis

Annals of Gastroenterological Surgery, EarlyView.
We retrospectively analyzed clinical data from patients who underwent hepatectomy for hepatocellular carcinoma (HCC) using LCA‐based grading system. These findings provide a new risk stratification framework for the design of precision surgery to treat patients with HCC.
Ling Liu, Jintao Zheng, Ye Wang, Chenao Yang, Jiachen Zhang, Changku Jia   +5 more
wiley   +1 more source

The Development and Psychometric Properties of a Doctoral Student Agency Scale. [PDF]

Behav Sci (Basel)
Huang L, Ruan Q, Wang K.
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Knowledge Distillation in Object Detection: A Survey from CNN to Transformer. [PDF]

Sensors (Basel)
Shehzadi T, Noor R, Ifza I, Liwicki M, Stricker D, Afzal MZ.   +5 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Enhancing deep learning interpretability for hand-crafted feature-guided histologic image classification via weak-to-strong generalization. [PDF]

J Med Imaging (Bellingham)
Fan Z, Lu C, Yue J, Anastasio M, Sun L, Wang X, Li H.   +6 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

A vision-language foundation model for Alzheimer's disease diagnosis using MRI and clinical data. [PDF]

Alzheimers Dement
Lin PJ, Jiang Z, Liu Y, Li W, Qi Y, Sun J, Ni S, Shu L, Xia M, Toga AW, Alzheimer's Disease Neuroimaging Initiative, for the Health and Aging Brain Study (HABS‐HD) Study Team.   +11 more
europepmc   +1 more source

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Advanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah, Muhammad Zulqarnain Zeb, Muhammad Husnain Zeb, Babar Shabbir   +3 more
wiley   +1 more source

Cross-lingual sparse-MoE distillation for efficient low-resource assamese-english and bodo-english translation. [PDF]

Sci Rep
Nath B, Gulzar Y.
europepmc   +1 more source